Wireless Device Identification Scheme Based on CSI Feature Fingerprints

In recent years, wireless networks have been widely used in healthcare, industry, education, and military applications. However, security threats for these networks are increasing. Traditional cryptographic authentication methods have several limitations, including restricted computational resources, vulnerabilities to quantum computing, and susceptibility to tampering. To address these challenges, a device fingerprint verification scheme based on physical layer information is proposed. This scheme leverages fingerprint features derived from Channel State Information (CSI) for device identification to prevent malicious Wi-Fi connections. The proposed scheme considers both stationary and mobile devices with the aim of improving the terminal identification accuracy and stability. For stationary devices with minimal interference in the authentication scenario, the CSI amplitude information matrix is used as the authentication fingerprint. For mobile devices, where the CSI information varies with device movement, the direct extraction of fingerprint information is infeasible. Instead, the fingerprint features are constructed by extracting the I/Q phase errors for device identification. Self-designed One-Class SupportVector Machine (SVM) based on Confidence Level (OSCL) and isolation Forest (iForest) based on Confidence Level (IFCL) models are employed to train the fingerprints generated by the two schemes, enabling accurate identification of the target devices. The scheme achieves identification accuracies of 99% and 74% for stationary and mobile devices, respectively. This scheme effectively complements cryptography-based device identification methods. Additionally, during the training phase, only positive data are utilized to address the unpredictability of abnormal device fingerprint information and enhance robustness.